OpenRaiser/Intern-Atlas huggingface dataset:
XAI Handbook: Towards a Unified Framework for Explainable AI
Venue: arXiv cs.AI 2021
Year: 2021

The field of explainable AI (XAI) has quickly become a thriving and prolific community. However, a silent, recurrent and acknowledged issue in this area is the lack of consensus regarding its terminology. In particular, each new contribution seems to rely on its own (and often intuitive) version of terms like "explanation" and "interpretation". Such disarray encumbers the consolidation of advances in the field towards the fulfillment of scientific and regulatory demands e.g., when comparing methods or establishing their compliance with respect to biases and fairness constraints. We propose a theoretical framework that not only provides concrete definitions for these terms, but it also outlines all steps necessary to produce explanations and interpretations. The framework also allows for existing contributions to be re-contextualized such that their scope can be measured, thus making them comparable to other methods. We show that this framework is compliant with desiderata on explanations, on interpretability and on evaluation metrics. We present a use-case showing how the framework can be used to compare LIME, SHAP and MDNet, establishing their advantages and shortcomings. Finally, we discuss relevant trends in XAI as well as recommendations for future work, all from the standpoint of our framework.
Desperately seeking the impact of learning analytics in education at scale: Marrying data analysis with teaching and learning
Venue: arXiv cs.CY 2021
Year: 2021

Learning analytics (LA) is argued to be able to improve learning outcomes, learner support and teaching. However, despite an increasingly expanding amount of student (digital) data accessible from various online education and learning platforms and the growing interest in LA worldwide as well as considerable research efforts already made, there is still little empirical evidence of impact on practice that shows the effectiveness of LA in education settings. Based on a selection of theoretical and empirical research, this chapter provides a critical discussion about the possibilities of collecting and using student data as well as barriers and challenges to overcome in providing data-informed support to educators' everyday teaching practices. We argue that in order to increase the impact of data-driven decision-making aimed at students' improved learning in education at scale, we need to better understand educators' needs, their teaching practices and the context in which these practices occur, and how to support them in developing relevant knowledge, strategies and skills to facilitate the data-informed process of digitalization of education.
REGINA - Reasoning Graph Convolutional Networks in Human Action Recognition
Venue: arXiv cs.CV 2021
Year: 2021

It is known that the kinematics of the human body skeleton reveals valuable information in action recognition. Recently, modeling skeletons as spatio-temporal graphs with Graph Convolutional Networks (GCNs) has been reported to solidly advance the state-of-the-art performance. However, GCN-based approaches exclusively learn from raw skeleton data, and are expected to extract the inherent structural information on their own. This paper describes REGINA, introducing a novel way to REasoning Graph convolutional networks IN Human Action recognition. The rationale is to provide to the GCNs additional knowledge about the skeleton data, obtained by handcrafted features, in order to facilitate the learning process, while guaranteeing that it remains fully trainable in an end-to-end manner. The challenge is to capture complementary information over the dynamics between consecutive frames, which is the key information extracted by state-of-the-art GCN techniques. Moreover, the proposed strategy can be easily integrated in the existing GCN-based methods, which we also regard positively. Our experiments were carried out in well known action recognition datasets and enabled to conclude that REGINA contributes for solid improvements in performance when incorporated to other GCN-based approaches, without any other adjustment regarding the original method. For reproducibility, the REGINA code and all the experiments carried out will be publicly available at https://github.com/DegardinBruno.
Quantifying the Impact of Boundary Constraint Handling Methods on Differential Evolution
Venue: arXiv cs.NE 2021
Year: 2021

Constraint handling is one of the most influential aspects of applying metaheuristics to real-world applications, which can hamper the search progress if treated improperly. In this work, we focus on a particular case - the box constraints, for which many boundary constraint handling methods (BCHMs) have been proposed. We call for the necessity of studying the impact of BCHMs on metaheuristics' performance and behavior, which receives seemingly little attention in the field. We target quantifying such impacts through systematic benchmarking by investigating 28 major variants of Differential Evolution (DE) taken from the modular DE framework (by combining different mutation and crossover operators) and $13$ commonly applied BCHMs, resulting in $28 \times 13 = 364$ algorithm instances after pairing DE variants with BCHMs. After executing the algorithm instances on the well-known BBOB/COCO problem set, we analyze the best-reached objective function value (performance-wise) and the percentage of repaired solutions (behavioral) using statistical ranking methods for each combination of mutation, crossover, and BBOB function group. Our results clearly show that the choice of BCHMs substantially affects the empirical performance as well as the number of generated infeasible solutions, which allows us to provide general guidelines for selecting an appropriate BCHM for a given scenario.
Salient Feature Extractor for Adversarial Defense on Deep Neural Networks
Venue: arXiv cs.CV 2021
Year: 2021

Recent years have witnessed unprecedented success achieved by deep learning models in the field of computer vision. However, their vulnerability towards carefully crafted adversarial examples has also attracted the increasing attention of researchers. Motivated by the observation that adversarial examples are due to the non-robust feature learned from the original dataset by models, we propose the concepts of salient feature(SF) and trivial feature(TF). The former represents the class-related feature, while the latter is usually adopted to mislead the model. We extract these two features with coupled generative adversarial network model and put forward a novel detection and defense method named salient feature extractor (SFE) to defend against adversarial attacks. Concretely, detection is realized by separating and comparing the difference between SF and TF of the input. At the same time, correct labels are obtained by re-identifying SF to reach the purpose of defense. Extensive experiments are carried out on MNIST, CIFAR-10, and ImageNet datasets where SFE shows state-of-the-art results in effectiveness and efficiency compared with baselines. Furthermore, we provide an interpretable understanding of the defense and detection process.
Towards Navigation by Reasoning over Spatial Configurations
Venue: arXiv cs.CL 2021
Year: 2021

We deal with the navigation problem where the agent follows natural language instructions while observing the environment. Focusing on language understanding, we show the importance of spatial semantics in grounding navigation instructions into visual perceptions. We propose a neural agent that uses the elements of spatial configurations and investigate their influence on the navigation agent's reasoning ability. Moreover, we model the sequential execution order and align visual objects with spatial configurations in the instruction. Our neural agent improves strong baselines on the seen environments and shows competitive performance on the unseen environments. Additionally, the experimental results demonstrate that explicit modeling of spatial semantic elements in the instructions can improve the grounding and spatial reasoning of the model.
Fit4CAD: A point cloud benchmark for fitting simple geometric primitives in CAD objects
Venue: arXiv cs.GR 2021
Year: 2021

We propose Fit4CAD, a benchmark for the evaluation and comparison of methods for fitting simple geometric primitives in point clouds representing CAD objects. This benchmark is meant to help both method developers and those who want to identify the best performing tools. The Fit4CAD dataset is composed by 225 high quality point clouds, each of which has been obtained by sampling a CAD object. The way these elements were created by using existing platforms and datasets makes the benchmark easily expandable. The dataset is already split into a training set and a test set. To assess performance and accuracy of the different primitive fitting methods, various measures are defined. To demonstrate the effective use of Fit4CAD, we have tested it on two methods belonging to two different categories of approaches to the primitive fitting problem: a clustering method based on a primitive growing framework and a parametric method based on the Hough transform.
A Tutorial to Sparse Code Multiple Access
Venue: arXiv cs.IT 2021
Year: 2021

Sparse Code Multiple Access (SCMA) is an enabling code-domain non-orthogonal multiple access (NOMA)scheme for massive connectivity and ultra low-latency in future machine-type communication networks. As an evolved variant of code division multiple access (CDMA), multiple users in SCMA are separated by assigning distinctive codebooks which display certain sparsity. At an SCMA receiver, efficient multiuser detection is carried out by employing the message passing algorithm (MPA) which exploits the sparsity of codebooks to achieve error rate performance approaching to that of the maximum likelihood receiver. Despite numerous research efforts on SCMA in recent years, a comprehensive and in-depth tutorial to SCMA is missing, to the best of our knowledge. To fill this gap and to stimulate more forthcoming research, we introduce the principles of SCMA encoding, codebook design, and MPA based decoding in a self-contained manner for layman researchers and engineers.
Privacy-preserving Logistic Regression with Secret Sharing
Venue: arXiv cs.LG 2021
Year: 2021

Logistic regression (LR) is a widely used classification method for modeling binary outcomes in many medical data classification tasks. Research that collects and combines datasets from various data custodians and jurisdictions can excessively benefit from the increased statistical power to support their analyzing goals. However, combining data from these various sources creates significant privacy concerns that need to be addressed. In this paper, we proposed secret sharing-based privacy-preserving logistic regression protocols using the Newton-Raphson method. Our proposed approaches are based on secure Multi-Party Computation (MPC) with different security settings to analyze data owned by several data holders. We conducted experiments on both synthetic data and real-world datasets and compared the efficiency and accuracy of them with those of an ordinary logistic regression model. Experimental results demonstrate that the proposed protocols are highly efficient and accurate. This study introduces iterative algorithms to simplify the federated training a logistic regression model in a privacy-preserving manner. Our implementation results show that our improved method can handle large datasets used in securely training a logistic regression from multiple sources.
Hierarchical Architectures in Reservoir Computing Systems
Venue: arXiv cs.ET 2021
Year: 2021

Reservoir computing (RC) offers efficient temporal data processing with a low training cost by separating recurrent neural networks into a fixed network with recurrent connections and a trainable linear network. The quality of the fixed network, called reservoir, is the most important factor that determines the performance of the RC system. In this paper, we investigate the influence of the hierarchical reservoir structure on the properties of the reservoir and the performance of the RC system. Analogous to deep neural networks, stacking sub-reservoirs in series is an efficient way to enhance the nonlinearity of data transformation to high-dimensional space and expand the diversity of temporal information captured by the reservoir. These deep reservoir systems offer better performance when compared to simply increasing the size of the reservoir or the number of sub-reservoirs. Low frequency components are mainly captured by the sub-reservoirs in later stage of the deep reservoir structure, similar to observations that more abstract information can be extracted by layers in the late stage of deep neural networks. When the total size of the reservoir is fixed, tradeoff between the number of sub-reservoirs and the size of each sub-reservoir needs to be carefully considered, due to the degraded ability of individual sub-reservoirs at small sizes. Improved performance of the deep reservoir structure alleviates the difficulty of implementing the RC system on hardware systems.
On Measuring the Diversity of Organizational Networks
Venue: arXiv cs.SI 2021
Year: 2021

The interaction patterns of employees in social and professional networks play an important role in the success of employees and organizations as a whole. However, in many fields there is a severe under-representation of minority groups; moreover, minority individuals may be segregated from the rest of the network or isolated from one another. While the problem of increasing the representation of minority groups in various fields has been well-studied, diver- sification in terms of numbers alone may not be sufficient: social relationships should also be considered. In this work, we consider the problem of assigning a set of employment candidates to positions in a social network so that diversity and overall fitness are maximized, and propose Fair Employee Assignment (FairEA), a novel algorithm for finding such a matching. The output from FairEA can be used as a benchmark by organizations wishing to evaluate their hiring and assignment practices. On real and synthetic networks, we demonstrate that FairEA does well at finding high-fitness, high-diversity matchings.
Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer
Venue: arXiv cs.CL 2021
Year: 2021

Scarcity of parallel data causes formality style transfer models to have scarce success in preserving content. We show that fine-tuning pre-trained language (GPT-2) and sequence-to-sequence (BART) models boosts content preservation, and that this is possible even with limited amounts of parallel data. Augmenting these models with rewards that target style and content -- the two core aspects of the task -- we achieve a new state-of-the-art.
Thompson Sampling for Gaussian Entropic Risk Bandits
Venue: arXiv cs.LG 2021
Year: 2021

The multi-armed bandit (MAB) problem is a ubiquitous decision-making problem that exemplifies exploration-exploitation tradeoff. Standard formulations exclude risk in decision making. Risknotably complicates the basic reward-maximising objectives, in part because there is no universally agreed definition of it. In this paper, we consider an entropic risk (ER) measure and explore the performance of a Thompson sampling-based algorithm ERTS under this risk measure by providing regret bounds for ERTS and corresponding instance dependent lower bounds.
Ready When You Are: Efficient Condition Variables via Delegated Condition Evaluation
Venue: arXiv cs.DC 2021
Year: 2021

Multi-thread applications commonly utilize condition variables for communication between threads. Condition variables allow threads to block and wait until a certain condition holds, and also enable threads to wake up their blocked peers notifying them about a change to the state of shared data. Quite often such notifications are delivered to all threads, while only a small number of specific threads is interested in it. This results in so-called futile wakeups, where threads receiving the notification wake up and resume their execution only to realize that the condition they are waiting for does not hold and they need to wait again. Those wakeups cause numerous context switches, increase lock contention and cache pressure, translating into lots of wasted computing cycles and energy. In this work, we propose to delegate conditions on which threads are waiting to the thread sending notifications. This enables the latter to evaluate the conditions and send the notification(s) only to the relevant thread(s), practically eliminating futile wakeups altogether. Our initial evaluation of this idea shows promising results, achieving 3-4x throughput improvement over legacy condition variables.
Counterfactual Interventions Reveal the Causal Effect of Relative Clause Representations on Agreement Prediction
Venue: arXiv cs.CL 2021
Year: 2021

When language models process syntactically complex sentences, do they use their representations of syntax in a manner that is consistent with the grammar of the language? We propose AlterRep, an intervention-based method to address this question. For any linguistic feature of a given sentence, AlterRep generates counterfactual representations by altering how the feature is encoded, while leaving intact all other aspects of the original representation. By measuring the change in a model's word prediction behavior when these counterfactual representations are substituted for the original ones, we can draw conclusions about the causal effect of the linguistic feature in question on the model's behavior. We apply this method to study how BERT models of different sizes process relative clauses (RCs). We find that BERT variants use RC boundary information during word prediction in a manner that is consistent with the rules of English grammar; this RC boundary information generalizes to a considerable extent across different RC types, suggesting that BERT represents RCs as an abstract linguistic category.
A Survey of Security Vulnerabilities in Ethereum Smart Contracts
Venue: arXiv cs.CR 2021
Year: 2021

Ethereum Smart Contracts based on Blockchain Technology (BT)enables monetary transactions among peers on a blockchain network independent of a central authorizing agency. Ethereum smart contracts are programs that are deployed as decentralized applications, having the building blocks of the blockchain consensus protocol. This enables consumers to make agreements in a transparent and conflict-free environment. However, there exist some security vulnerabilities within these smart contracts that are a potential threat to the applications and their consumers and have shown in the past to cause huge financial losses. In this study, we review the existing literature and broadly classify the BT applications. As Ethereum smart contracts find their application mostly in e-commerce applications, we believe these are more commonly vulnerable to attacks. In these smart contracts, we mainly focus on identifying vulnerabilities that programmers and users of smart contracts must avoid. This paper aims at explaining eight vulnerabilities that are specific to the application level of BT by analyzing the past exploitation case scenarios of these security vulnerabilities. We also review some of the available tools and applications that detect these vulnerabilities in terms of their approach and effectiveness. We also investigated the availability of detection tools for identifying these security vulnerabilities and lack thereof to identify some of them
Generalized LR-drawings of trees
Venue: arXiv cs.CG 2021
Year: 2021

The LR-drawing-method is a method of drawing an ordered rooted binary tree based on drawing one root-to-leaf path on a vertical line and attaching recursively obtained drawings of the subtrees on the left and right. In this paper, we study how to generalize this drawing-method to trees of higher arity. We first prove that (with some careful modifications) the proof of existence of a special root-to-leaf path transfers to trees of higher arity. Then we use such paths to obtain generalized LR-drawings of trees of arbitrary arity.
Methods Included: Standardizing Computational Reuse and Portability with the Common Workflow Language
Venue: arXiv cs.DC 2021
Year: 2021

Computational Workflows are widely used in data analysis, enabling innovation and decision-making. In many domains (bioinformatics, image analysis, & radio astronomy) the analysis components are numerous and written in multiple different computer languages by third parties. However, many competing workflow systems exist, severely limiting portability of such workflows, thereby hindering the transfer of workflows between different systems, between different projects and different settings, leading to vendor lock-ins and limiting their generic re-usability. Here we present the Common Workflow Language (CWL) project which produces free and open standards for describing command-line tool based workflows. The CWL standards provide a common but reduced set of abstractions that are both used in practice and implemented in many popular workflow systems. The CWL language is declarative, which allows expressing computational workflows constructed from diverse software tools, executed each through their command-line interface. Being explicit about the runtime environment and any use of software containers enables portability and reuse. Workflows written according to the CWL standards are a reusable description of that analysis that are runnable on a diverse set of computing environments. These descriptions contain enough information for advanced optimization without additional input from workflow authors. The CWL standards support polylingual workflows, enabling portability and reuse of such workflows, easing for example scholarly publication, fulfilling regulatory requirements, collaboration in/between academic research and industry, while reducing implementation costs. CWL has been taken up by a wide variety of domains, and industries and support has been implemented in many major workflow systems.
Post-processing Multi-Model Medium-Term Precipitation Forecasts Using Convolutional Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

The goal of this study was to improve the post-processing of precipitation forecasts using convolutional neural networks (CNNs). Instead of post-processing forecasts on a per-pixel basis, as is usually done when employing machine learning in meteorological post-processing, input forecast images were combined and transformed into probabilistic output forecast images using fully convolutional neural networks. CNNs did not outperform regularized logistic regression. Additionally, an ablation analysis was performed. Combining input forecasts from a global low-resolution weather model and a regional high-resolution weather model improved performance over either one.
Slicing-Based AI Service Provisioning on Network Edge
Venue: arXiv cs.NI 2021
Year: 2021

Edge intelligence leverages computing resources on network edge to provide artificial intelligence (AI) services close to network users. As it enables fast inference and distributed learning, edge intelligence is envisioned to be an important component of 6G networks. In this article, we investigate AI service provisioning for supporting edge intelligence. First, we present the features and requirements of AI services. Then, we introduce AI service data management, and customize network slicing for AI services. Specifically, we propose a novel resource pooling method to jointly manage service data and network resources for AI services. A trace-driven case study demonstrates the effectiveness of the proposed resource pooling method. Through this study, we illustrate the necessity, challenge, and potential of AI service provisioning on network edge.